Immune-related gene-based prognostic index for predicting survival and immunotherapy outcomes in colorectal carcinoma

Liang, Zhongqing; Sun, Ruolan; Tu, Ping; Liang, Yan; Li, Liang; Liu, Fuyan; Bian, Yong; Yin, Gang; Zhao, Fan; Jiang, Mingchen; Gu, Junfei; Tang, Decai

doi:10.3389/fimmu.2022.944286

Cited by 6 publications

(5 citation statements)

References 58 publications

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“…Lin et al (31) found a strong correlation between the genes of the IRGPI and previously identified prognostic and clinically relevant indicators (OS, TNM stage, gender, age). In addition, Liang et al (32) illustrated that the interaction of immune-related genes based on IRGPI calculations may influence CRC formation and development. In contrast, more genes were included in our study with better predictive accuracy (the AUCs for 1-, 3-, and 5-year survival were 0.767, 0.800, 0.808 vs. 0.723, 0.724, 0.744).…”

Section: Discussionmentioning

confidence: 99%

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An immune-related gene prognostic index for predicting prognosis in patients with colorectal cancer

Wirth

Schardey

et al. 2023

Front. Immunol.

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BackgroundColorectal cancer (CRC) is one of the most common solid malignant burdens worldwide. Cancer immunology and immunotherapy have become fundamental areas in CRC research and treatment. Currently, the method of generating Immune-Related Gene Prognostic Indices (IRGPIs) has been found to predict patient prognosis as an immune-related prognostic biomarker in a variety of tumors. However, their role in patients with CRC remains mostly unknown. Therefore, we aimed to establish an IRGPI for prognosis evaluation in CRC.MethodsRNA-sequencing data and clinical information of CRC patients were retrieved from The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) databases as training and validation sets, respectively. Immune-related gene data was obtained from the ImmPort and InnateDB databases. The weighted gene co-expression network analysis (WGCNA) was used to identify hub immune-related genes. An IRGPI was then constructed using Cox regression methods. Based on the median risk score of IRGPI, patients could be divided into high-risk and low-risk groups. To further investigate the immunologic differences, Gene set variation analysis (GSVA) studies were conducted. In addition, immune cell infiltration and related functional analysis were used to identify the differential immune cell subsets and related functional pathways.ResultsWe identified 49 immune-related genes associated with the prognosis of CRC, 17 of which were selected for an IRGPI. The IRGPI model significantly differentiates the survival rates of CRC patients in the different groups. The IRGPI as an independent prognostic factor significantly correlates with clinico-pathological factors such as age and tumor stage. Furthermore, we developed a nomogram to improve the clinical utility of the IRGPI score. Immuno-correlation analysis in different IRGPI groups revealed distinct immune cell infiltration (CD4+ T cells resting memory) and associated pathways (macrophages, Type I IFNs responses, iDCs.), providing new insights into the tumor microenvironment. At last, drug sensitivity analysis revealed that the high-risk IRGPI group was sensitive to 11 and resistant to 15 drugs.ConclusionOur study established a promising immune-related risk model for predicting survival in CRC patients. This could help to better understand the correlation between immunity and the prognosis of CRC providing a new perspective for personalized treatment of CRC.

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Section: Discussionmentioning

confidence: 99%

“…In addition, Liang et al. ( 32 ) illustrated that the interaction of immune-related genes based on IRGPI calculations may influence CRC formation and development. In contrast, more genes were included in our study with better predictive accuracy (the AUCs for 1-, 3-, and 5-year survival were 0.767, 0.800, 0.808 vs. 0.723, 0.724, 0.744).…”

Section: Discussionmentioning

confidence: 99%

An immune-related gene prognostic index for predicting prognosis in patients with colorectal cancer

Wirth

Schardey

et al. 2023

Front. Immunol.

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“… multiple myeloma [ 57 ] miR-20a miR-20a downregulated MICA expression, reduced NK cell cytotoxicity, and was NKG2D dependent in cancer cells to promote tumour growth. colorectal cancer [ 60 , 61 ] miR-20a The regulation of MICA by miR-20a in ovarian cancer is similar to that in colorectal cancer. ovarian cancer [ 62 ] miR-17-92 cluster (miR-20a miR-20b miR-93 miR-106b) The regulation of MICA by miR-17-92 cluster in breast cancer is similar to that in ovarian and colorectal cancer.…”

Section: Introductionmentioning

confidence: 99%

“…Colorectal cancer (CRC) is among the most common fatal cancers worldwide [ 58 , 59 ]. The miR-20a expression level was significantly increased in CRC tissues [ 60 , 61 ]. Ovarian cancer (OC) is the most lethal malignancy in a woman’s reproductive system, and immunotherapy has a limited impact on treatment outcomes.…”

Section: Introductionmentioning

confidence: 99%

Novel role of immune-related non-coding RNAs as potential biomarkers regulating tumour immunoresponse via MICA/NKG2D pathway

Zhang,

Luo,

et al. 2023

Biomark Res

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Major histocompatibility complex class I related chain A (MICA) is an important and stress-induced ligand of the natural killer group 2 member D receptor (NKG2D) that is expressed in various tumour cells. Given that the MICA/NKG2D signalling system is critically embedded in the innate and adaptive immune responses, it is particularly involved in the surveillance of cancer and viral infections. Emerging evidence has revealed the important roles of non-coding RNAs (ncRNAs) including microRNAs (miRNAs), long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) in different cancer types. We searched for all relevant publications in the PubMed, Scopus and Web of Science database using the keywords ncRNA, MICA, NKG2D, cancer, and miRNAs. All relevant studies published from 2008 to the 2023 were retrieved and collated. Notably, we found that miRNAs can target to NKG2D mRNA and MICA mRNA 3’-untranslated regions (3’-UTR), leading to translation inhibition of NKG2D and MICA degradation. Several immune-related MICA/NKG2D pathways may be dysregulated in cancer with aberrant miRNA expressions. At the same time, the competitive endogenous RNA (ceRNA) hypothesis holds that circRNAs, lncRNAs, and mRNAs induce an abnormal MICA expression by directly targeting downstream miRNAs to mediate mRNA suppression in cancer. This review summarizes the novel mechanism of immune escape in the ncRNA-related MICA/NKG2D pathway mediated by NK cells and cancer cells. Moreover, we identified the miRNA-NKG2D, miRNA-MICA and circRNA/lncRNA/mRNA-miRNA-mRNA/MICA axis. Thus, we were particularly concerned with the regulation of mediated immune escape in the MICA/NKG2D pathway by ncRNAs as potential therapeutic targets and diagnostic biomarkers of immunity and cancer.

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“…Conception and design: ZL 1 and YD 3,4 . Administrative support: JZ 2 , RX 3,4 , JZ 5,6 and YJ 5,6 . Collection and assembly of data: ZL 1 .…”

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confidence: 99%

Identification of Immune-Related Genes and Construction of Metastasis Prediction Model for Non-Small Cell Lung Cancer

LIAO,

Zhou,

Xia

et al. 2023

Preprint

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Objective: The aim of the study was to identify the immune-related genes (IRGs) associated with non-small cell lung cancer (NSCLC) metastasis, and establish a risk score prediction model. Methods: Gene expression information and clinical data of NSCLC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened based on tumor group and normal group. DEGs were intersected with IRGs from the ImmPort database to obtain differentially expressed IRGs (DEIRGs). Weighted gene coexpression network analysis (WGCNA) was applied to determine the hub DEIRGs (HubDEIRGs) related to immune scores. Risk score was calculated based on the significant HubDEIRGs through logistic regression analyses. Logistic regression analysis was performed to analyze the influencing factors for metastasis with age, gender, T stage and risk score as covariates. A metastasis risk nomogram was constructed. The correlation between risk score and immune cells infiltration was examined. Results: A total of 477 HubDEIRGs were identified. PDK1, PROC, IL11, SH2D1B, S100A5, AGT, WFDC2, CRHR2 and EREG were metastasis-associated immune genes. Age, T stage and risk score served as independent risk factors for metastasis. The areas under the curve (AUC) of the nomogram were 0.714 and 0.643 in the training and validation sets. The calibration curve was close to the ideal diagonal line. The high-risk group had a greater degree of immune infiltration than the low-risk group. Conclusion: The risk scoring model for predicting the risk of metastasis in NSCLC patients based on 9 immune genes in this study had importantly potential clinical application value.

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Immune-related gene-based prognostic index for predicting survival and immunotherapy outcomes in colorectal carcinoma

Cited by 6 publications

References 58 publications

An immune-related gene prognostic index for predicting prognosis in patients with colorectal cancer

An immune-related gene prognostic index for predicting prognosis in patients with colorectal cancer

Novel role of immune-related non-coding RNAs as potential biomarkers regulating tumour immunoresponse via MICA/NKG2D pathway

Identification of Immune-Related Genes and Construction of Metastasis Prediction Model for Non-Small Cell Lung Cancer

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